Improving the efficiency of minimal model generation by extracting branching lemmas

Ryuzo Hasegawa, Hiroshi Fujita, Miyuki Koshimura

Research output: Contribution to journalArticle

Abstract

We present an efficient method for minimal model generation. The method employs branching assumptions and lemmas so as to prune branches that lead to nonminimal models, and to reduce minimality tests on obtained models. Branching lemmas are extracted from a subproof of a disjunct, and work as factorization. This method is applicable to other approaches such as Bry's constrained search or Niemelä's groundedness test, and greatlyimpro ves their efficiency. We implemented MM-MGTP based on the method. Experimental results with MM-MGTP show a remarkable speedup compared to MM-SATCHMO.

Original languageEnglish
Pages (from-to)234-245
Number of pages12
JournalTransactions of the Japanese Society for Artificial Intelligence
Volume16
Issue number2
DOIs
Publication statusPublished - Dec 1 2001

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

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